
# Import Packages
import pandas as pd
import numpy as np
from pandas_profiling import ProfileReport
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import roc_auc_score


# Read data from files
df_train = pd.read_csv("2022_train.csv")
df_test = pd.read_csv("2022_test.csv")
df_test2 = pd.read_csv("2022_test.csv")
df_train.head()


df_test.head()


# Perform some EDA
ProfileReport(df_train, title="EDA of training data")

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# Feature Engineering
# Drop columns and cube root the AST
df_train_short = df_train.drop(['GP', 'MIN', 'PTS', 'FGM', 'FGA', '3P Made', '3PA', 'FTM', 'FTA', 'OREB', 'DREB', 'TOV', 'BLK', 'STL', 'REB'], axis=1)
df_train_short['AST TX'] = df_train_short['AST']**(1/3)
df_train_short.drop(['Id', 'AST'], axis = 1, inplace= True)


# Create target variable y from dataset and X
y = df_train_short.pop('TARGET_5Yrs')
X = df_train_short


# Scale the X values
scaler = StandardScaler()
X = scaler.fit_transform(X)


# Split into training and validation datasets at 80% training. Stratified data as well
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.3, stratify=y)


# Define model and predict the values
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=2)

y_val_pred_knn = knn.fit(X_train, y_train).predict(X_val)


# Print the AUC score
auc_score3 = roc_auc_score(y_val, y_val_pred_knn)
print(auc_score3)

0.5505198528555271


# Now to try it on test data

df_test_pred = df_test.drop(['GP', 'MIN', 'PTS', 'FGM', 'FGA', '3P Made', '3PA', 'FTM', 'FTA', 'OREB', 'DREB', 'TOV', 'BLK', 'STL', 'REB'], axis=1)
df_test_pred['AST TX'] = df_test_pred['AST']**(1/3)
df_test_pred.drop(['Id', 'AST'], axis = 1, inplace= True)


# Predict the probabilities, then add it to a dataframe with the IDs from the test set
y_test_predictions = knn.fit(X_train, y_train).predict_proba(df_test_pred)
probabilities = y_test_predictions[:,1]
final = pd.DataFrame({'Id':df_test.Id, 'TARGET_5Yrs':probabilities})
final


# Save to CSV for upload to Kaggle
final.to_csv('2022_timwang_firsttry.csv', index = False)

	Id	GP	MIN	PTS	FGM	FGA	FG%	3P Made	3PA	3P%	...	FTA	FT%	OREB	DREB	REB	AST	STL	BLK	TOV	TARGET_5Yrs
0	3799	80	24.3	7.8	3.0	6.4	45.7	0.1	0.3	22.6	...	2.9	72.1	2.2	2.0	3.8	3.2	1.1	0.2	1.6	1
1	3800	75	21.8	10.5	4.2	7.9	55.1	-0.3	-1.0	34.9	...	3.6	67.8	3.6	3.7	6.6	0.7	0.5	0.6	1.4	1
2	3801	85	19.1	4.5	1.9	4.5	42.8	0.4	1.2	34.3	...	0.6	75.7	0.6	1.8	2.4	0.8	0.4	0.2	0.6	1
3	3802	63	19.1	8.2	3.5	6.7	52.5	0.3	0.8	23.7	...	1.5	66.9	0.8	2.0	3.0	1.8	0.4	0.1	1.9	1
4	3803	63	17.8	3.7	1.7	3.4	50.8	0.5	1.4	13.7	...	0.5	54.0	2.4	2.7	4.9	0.4	0.4	0.6	0.7	1

	Id	GP	MIN	PTS	FGM	FGA	FG%	3P Made	3PA	3P%	FTM	FTA	FT%	OREB	DREB	REB	AST	STL	BLK	TOV
0	0	56	9.1	4.0	1.6	3.7	43.7	0.1	0.3	7.3	0.7	1.2	63.4	1.2	0.8	1.7	0.4	0.2	0.3	0.8
1	1	43	19.3	10.1	3.7	8.1	46.0	0.6	1.7	35.1	1.8	2.5	75.3	0.5	0.9	1.5	3.5	0.6	0.0	1.8
2	2	82	33.9	11.3	4.9	10.6	45.6	0.5	1.9	44.8	1.8	2.7	71.2	1.3	3.3	4.5	2.5	1.3	0.3	2.0
3	3	86	44.7	18.8	6.8	15.9	42.9	0.5	1.8	13.5	4.5	6.3	70.9	1.5	3.2	5.0	4.1	0.9	0.1	3.6
4	4	58	12.3	4.7	1.6	4.0	40.0	0.5	1.7	38.7	1.1	1.3	76.9	0.2	0.6	0.9	1.5	0.5	-0.4	0.9

	Id	TARGET_5Yrs
0	0	1.0
1	1	1.0
2	2	1.0
3	3	1.0
4	4	1.0
...	...	...
3794	3794	1.0
3795	3795	1.0
3796	3796	1.0
3797	3797	1.0
3798	3798	1.0